Elementary Linear Algebra

Elementary Linear Algebra 10th edition gives an elementary treatment of linear algebra that is suitable for a first course for undergraduate students.  The aim is to present the fundamentals of linear algebra in the clearest possible way; pedagogy is the main consideration.  Calculus is not a prerequisite, but there are clearly labeled exercises and examples (which can be omitted without loss of continuity) for students who have studied calculus.   Technology also is not required, but for those who would like to use MATLAB, Maple, or Mathematica, or calculators with linear algebra capabilities, exercises are included at the ends of chapters that allow for further exploration using those tools. This title is available with WileyPLUS. This online teaching and learning environment integrates the entire digital textbook with the most effective instructor and student resources to fit every learning style.  With WileyPLUS:  Students achieve concept mastery in a rich, structured environment that’s available 24/7 Instructors personalize and manage their course more effectively with assessment, assignments, grade tracking, and more. WileyPLUS can complement the textbook or replace the printed text altogether

Table of Contents:
CHAPTER 1 Systems of Linear Equations and Matrices 1 1.1 Introduction to Systems of Linear Equations 2 1.2 Gaussian Elimination 11 1.3 Matrices and Matrix Operations 25 1.4 Inverses; Algebraic Properties of Matrices 38 1.5 Elementary Matrices and a Method for Finding A−1 51 1.6 More on Linear Systems and Invertible Matrices 60 1.7 Diagonal, Triangular, and Symmetric Matrices 66 1.8 Application: Applications of Linear Systems 73 1.9 Application: Leontief Input-Output Models 85 CHAPTER 2 Determinants 93 2.1 Determinants by Cofactor Expansion 93 2.2 Evaluating Determinants by Row Reduction 100 2.3 Properties of Determinants; Cramer’s Rule 106 CHAPTER 3 Euclidean Vector Spaces 119 3.1 Vectors in 2-Space, 3-Space, and n-Space 119 3.2 Norm, Dot Product, and Distance in Rn130 3.3 Orthogonality 143 3.4 The Geometry of Linear Systems 152 3.5 Cross Product 161 CHAPTER 4 General Vector Spaces 171 4.1 Real Vector Spaces 171 4.2 Subspaces 179 4.3 Linear Independence 190 4.4 Coordinates and Basis 200 4.5 Dimension 209 4.6 Change of Basis 217 4.7 Row Space, Column Space, and Null Space 225 4.8 Rank, Nullity, and the Fundamental Matrix Spaces 237 4.9 Matrix Transformations from Rn to Rm 247 4.10 Properties of Matrix Transformations 263 4.11 Application: Geometry of Matrix Operators on R2 273 4.12 Application: Dynamical Systems and Markov Chains 282 CHAPTER 5 Eigenvalues and Eigenvectors 295 5.1 Eigenvalues and Eigenvectors 295 5.2 Diagonalization 305 5.3 Complex Vector Spaces 315 5.4 Application: Differential Equations 327 CHAPTER 6 Inner Product Spaces 335 6.1 Inner Products 335 6.2 Angle and Orthogonality in Inner Product Spaces 345 6.3 Gram–Schmidt Process; QR-Decomposition 352 6.4 Best Approximation; Least Squares 366 6.5 Application: Least Squares Fitting to Data 376 6.6 Application: Function Approximation; Fourier Series 382 CHAPTER 7 Diagonalization and Quadratic Forms 389 7.1 Orthogonal Matrices 389 7.2 Orthogonal Diagonalization 397 7.3 Quadratic Forms 405 7.4 Optimization Using Quadratic Forms 417 7.5 Hermitian, Unitary, and Normal Matrices 424 CHAPTER 8 Linear Transformations 433 8.1 General Linear Transformations 433 8.2 Isomorphism 445 8.3 Compositions and Inverse Transformations 452 8.4 Matrices for General Linear Transformations 458 8.5 Similarity 468 CHAPTER 9 Numerical Methods 477 9.1 LU-Decompositions 477 9.2 The Power Method 487 9.3 Application: Internet Search Engines 496 9.4 Comparison of Procedures for Solving Linear Systems 501 9.5 Singular Value Decomposition 506 9.6 Application: Data Compression Using Singular Value Decomposition 514 APPENDIX A How to Read Theorems 519 APPENDIX B Complex Numbers 521 Answers to Exercises 529 Index 559